mlsanityData to 7 Oct 2026

Perceptual Contrast Stretching on Target Feature for Speech Enhancement

arXiv 2203.17152 ↗First posted 31 Mar 2022cs.SD cs.CL eess.AScited by 6

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Abstract (arXiv)

Speech enhancement (SE) performance has improved considerably owing to the use of deep learning models as a base function. Herein, we propose a perceptual contrast stretching (PCS) approach to further improve SE performance. The PCS is derived based on the critical band importance function and is applied to modify the targets of the SE model. Specifically, the contrast of target features is stretched based on perceptual importance, thereby improving the overall SE performance. Compared with post-processing-based implementations, incorporating PCS into the training phase preserves performance and reduces online computation. Notably, PCS can be combined with different SE model architectures and training criteria. Furthermore, PCS does not affect the causality or convergence of SE model training. Experimental results on the VoiceBank-DEMAND dataset show that the proposed method can achieve state-of-the-art performance on both causal (PESQ score = 3.07) and noncausal (PESQ score = 3.35) SE tasks.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 14 of its 41 references that are in the map. First posted on arXiv 31 Mar 2022.

Builds on

14 of 41 references.

References · 14

  1. 2021SpeechBrain: A General-Purpose Speech Toolkitcited by 404
  2. 2020Real Time Speech Enhancement in the Waveform Domaincited by 183
  3. 2017SEGAN: Speech Enhancement Generative Adversarial Networkcited by 165
  4. 2021MetricGAN+: An Improved Version of MetricGAN for Speech Enhancementcited by 114
  5. 2019MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancementcited by 104
  6. 2019T-GSA: Transformer with Gaussian-weighted self-attention for speech enhancementcited by 34
  7. 2021DPT-FSNet: Dual-path Transformer Based Full-band and Sub-band Fusion Network for Speech Enhancementcited by 33
  8. 2017End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networkscited by 26
  9. 2018Speech Denoising with Deep Feature Lossescited by 26
  10. 2020A Perceptually-Motivated Approach for Low-Complexity, Real-Time Enhancement of Fullband Speechcited by 23
  11. 2015Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separationcited by 20
  12. 2017Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verificationcited by 19
2 more
  1. 2018Convolutional-Recurrent Neural Networks for Speech Enhancementcited by 17
  2. 2020Exploring the Best Loss Function for DNN-Based Low-latency Speech Enhancement with Temporal Convolutional Networkscited by 10

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 3; 2025: 2.

6 papers.

  1. 2024An Investigation of Incorporating Mamba for Speech Enhancementcited by 61
  2. 2024Mamba-SEUNet: Mamba UNet for Monaural Speech Enhancementcited by 10
  3. 2022SCP-GAN: Self-Correcting Discriminator Optimization for Training Consistency Preserving Metric GAN on Speech Enhancement Taskscited by 8
  4. 2025EDNet: A Versatile Speech Enhancement Framework with Gating Mamba Mechanism and Phase Shift-Invariant Trainingcited by 3
  5. 2024Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challengecited by 0
  6. 2025Magnitude-Phase Dual-Path Speech Enhancement Network based on Self-Supervised Embedding and Perceptual Contrast Stretch Boostingcited by 0